IT6006 DATA ANALYTICS | syllabus (ELECTIVE-III)

IT6006    DATA ANALYTICS L T P C   3 0 0 3

                                                   

OBJECTIVES:

The Student should be made to:
 Be exposed to big data
 Learn the different ways of Data Analysis
 Be familiar with data streams
 Learn the mining and clustering
 Be familiar with the visualization

UNIT I      INTRODUCTION TO BIG DATA    (8)

Introduction to Big Data Platform – Challenges of conventional systems - Web data – Evolution of
Analytic scalability, analytic processes and tools, Analysis vs reporting - Modern data analytic tools,
Stastical concepts: Sampling distributions, resampling, statistical inference, prediction error.

UNIT II      DATA ANALYSIS    (12)

Regression modeling, Multivariate analysis, Bayesian modeling, inference and Bayesian networks,
Support vector and kernel methods, Analysis of time series: linear systems analysis, nonlinear
dynamics - Rule induction - Neural networks: learning and generalization, competitive learning,
principal component analysis and neural networks; Fuzzy logic: extracting fuzzy models from data,
fuzzy decision trees, Stochastic search methods.

UNIT III      MINING DATA STREAMS    (8)

Introduction to Streams Concepts – Stream data model and architecture - Stream Computing,
Sampling data in a stream – Filtering streams – Counting distinct elements in a stream – Estimating
moments – Counting oneness in a window – Decaying window - Realtime Analytics Platform(RTAP)
applications - case studies - real time sentiment analysis, stock market predictions.

UNIT IV      FREQUENT ITEMSETS AND CLUSTERING    (9)

Mining Frequent itemsets - Market based model – Apriori Algorithm – Handling large data sets in Main
memory – Limited Pass algorithm – Counting frequent itemsets in a stream – Clustering Techniques –
Hierarchical – K- Means – Clustering high dimensional data – CLIQUE and PROCLUS – Frequent
pattern based clustering methods – Clustering in non-euclidean space – Clustering for streams and
Parallelism.

UNIT V      FRAMEWORKS AND VISUALIZATION    (8)

MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases - S3 - Hadoop Distributed file
systems – Visualizations - Visual data analysis techniques, interaction techniques; Systems and
applications:

                                                                                                                           TOTAL: 45 PERIODS

OUTCOMES:

The student should be made to:
 Apply the statistical analysis methods.
 Compare and contrast various soft computing frameworks.
 Design distributed file systems.
 Apply Stream data model.
 Use Visualisation techniques

TEXT BOOKS:

1. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007.
2. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University
Press, 2012.

REFERENCES:

1. Bill Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with
advanced analystics, John Wiley & sons, 2012.
2. Glenn J. Myatt, Making Sense of Data, John Wiley & Sons, 2007 Pete Warden, Big Data
Glossary, O’Reilly, 2011.
3. Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier,
Reprinted 2008.

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